Identification of abnormalities or anomalies in images is particularly useful in the field of medicine to diagnose a number of potentially serious and deadly medical conditions. The identification of abnormalities in medical images, such as conventional X-rays, magnetic resonance imaging (MRI) scans and computer tomography (CT) scans, initially relied upon the talents of a skilled clinician to view an image and manually identify the abnormalities within the image. As even the most advanced imaging technologies have become more readily available and affordable, however, the number of images generated for medical diagnostic purposes has increased dramatically over the years. It soon became apparent that automated methods and systems would have to be developed in order to decrease the amount of time required for a clinician to review an individual image. By prescreening images using automated methods and systems, a skilled clinician can review more images within a given time frame, which results in greater efficiency and an overall reduction in the expensive of reading and interpreting medical images.
In view of the above, efforts have been made in the field of medical imaging technology to develop automated systems capable of imaging a particular area of the body and detecting potential abnormalities within the imaged area. For example, U.S. Pat. No. 7,103,224; entitled: “Method and System for Automatic Identification and Quantification of Abnormal Anatomical Structures in Medical Images”, by Edward Ashton; discloses a system that detects structures that are similar to an exemplar structure in order to detect lesions in images scans. In any such automated system, it is preferably to have identified abnormalities highlighted or otherwise visually indicated to allow a skilled clinician to quickly focus on areas of potential interest within an image. The skilled clinician can then identify whether the potential abnormality is significant and related to a potentially harmful medical condition or whether the potential abnormality is not significant and can be dismissed without further study. Thus, a great deal of the skilled clinician's time can be saved, even if the automated system does not positively identify abnormalities as harmful conditions, by quickly directing the clinician to those areas of the image most likely to contain abnormalities requiring the clinician's attention.
In conventional imaging systems in which abnormalities are identified, the systems have generally focused on utilizing processes and techniques that identify structures associated with specific types of abnormalities such as tumors or lesions. Image processing techniques employed in conventional MRI's, for example, are often focused on identifying the specific morphology of tumors or other abnormal anatomical features within an image. Attempting to identify a structure of tumor, however, can be extremely difficult due to the wide range and variation in the morphology of tumors, making it difficult to create a robust system capable of detecting a wide range of abnormalities. Thus, automated systems based on identification and separation of abnormal structures from known normal structures tend to be complex in nature, as a great deal of effort must be made to model and define what an abnormal structure is compared with a normal structure.
In view of the above, it would be desirable to provide a method and apparatus for identifying abnormalities within images that does not rely upon the identification of the structure of the abnormality itself.